Load Necessary Libraries

library(tidyverse)

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## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
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Filter for Selected Minerals

# List of selected minerals
selected_minerals <- c(
  "Aluminum", "Antimony", "Arsenic", "Barite", "Beryllium", "Bismuth", "Cerium", "Cesium", 
  "Chromium", "Cobalt", "Dysprosium", "Erbium", "Europium", "Fluorspar", "Gadolinium", 
  "Gallium", "Germanium", "Graphite", "Hafnium", "Holmium", "Indium", "Iridium", "Lanthanum", 
  "Lithium", "Lutetium", "Magnesium", "Manganese", "Neodymium", "Nickel", "Niobium", 
  "Palladium", "Platinum", "Praseodymium", "Rhodium", "Rubidium", "Ruthenium", "Samarium", 
  "Scandium", "Tantalum", "Tellurium", "Terbium", "Thulium", "Tin", "Titanium", "Tungsten", 
  "Vanadium", "Ytterbium", "Yttrium", "Zinc", "Zirconium"
)

# Load datasets (update paths as needed)
reliance_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_Fig2_Net_Import_Reliance.csv")

source_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_Fig3_Major_Import_Sources.csv") 

end_use_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_T4_Critical_Minerals_End_Use.csv") 

domestic_production_gap <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_T5_Critical_Minerals_Salient.csv")

Illustrating strategic use of criticl menerals

# Load necessary libraries
library(ggplot2)
library(dplyr)

# Define data: Critical minerals and their strategic uses
minerals_data <- data.frame(
  Mineral = c(
    "Aluminum", "Antimony", "Arsenic", "Barite", "Beryllium", "Bismuth", "Cerium",
    "Cesium", "Chromium", "Cobalt", "Dysprosium", "Erbium", "Europium", "Fluorspar",
    "Gadolinium", "Gallium", "Germanium", "Graphite", "Hafnium", "Holmium", "Indium",
    "Iridium", "Lanthanum", "Lithium", "Lutetium", "Magnesium", "Manganese", 
    "Neodymium", "Nickel", "Niobium", "Palladium", "Platinum", "Praseodymium", 
    "Rhodium", "Rubidium", "Ruthenium", "Samarium", "Scandium", "Tantalum", 
    "Tellurium", "Terbium", "Thulium", "Tin", "Titanium", "Tungsten", "Vanadium", 
    "Ytterbium", "Yttrium", "Zinc", "Zirconium"
  ),
  Use_Category = c(
    "Economy", "Defense", "Technology", "Energy", "Defense", "Technology", 
    "Technology", "Research", "Economy", "Technology", "Technology", "Technology", 
    "Technology", "Economy", "Technology", "Technology", "Technology", 
    "Energy", "Defense", "Technology", "Technology", "Energy", "Energy", 
    "Technology", "Technology", "Economy", "Economy", "Technology", "Economy", 
    "Defense", "Economy", "Economy", "Technology", "Energy", "Technology", 
    "Technology", "Defense", "Technology", "Technology", "Energy", "Technology", 
    "Energy", "Economy", "Economy", "Economy", "Technology", "Technology", 
    "Economy", "Technology", "Technology"
  ),
  Importance = c(
    80, 50, 30, 40, 60, 30, 45, 20, 70, 65, 55, 50, 45, 60, 
    50, 55, 40, 60, 35, 40, 50, 30, 60, 80, 25, 70, 70, 50, 
    60, 45, 55, 60, 50, 30, 35, 40, 50, 45, 60, 35, 50, 45, 
    60, 70, 50, 45, 55, 65, 60, 70
  )
)

# Add jitter to separate overlapping points
ggplot(minerals_data, aes(x = Use_Category, y = Importance, color = Use_Category)) +
  geom_point(position = position_jitter(width = 0.4, height = 5), alpha = 0.7, size = 3) +  # Add jitter to y-axis
  geom_text(aes(label = Mineral), position = position_jitter(width = 0.2, height = 2), 
            vjust = 0, hjust = 1.5, size = 2, color = "black") +  # Jitter text for better readability
  labs(
    title = "Strategic Uses of Critical Minerals",
    x = "Use Category",
    y = "Relative Importance",
    color = "Use Category"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
    axis.text.y = element_text(size = 10),
    plot.title = element_text(size = 14, face = "bold")
  )

Applications of Critical Minerals

This plot highlights the primary applications of critical minerals.

# Load the data (update the file path as needed)
end_use_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_T4_Critical_Minerals_End_Use.csv")

# Count the primary applications and create a bar chart
end_use_data %>%
  count(Primary.Applications, sort = TRUE) %>%
  head(10) %>%
  ggplot(aes(x = reorder(Primary.Applications, -n), y = n)) +
  geom_bar(stat = "identity", fill = "#117733") +
  coord_flip() +
  labs(
    title = "Primary Applications of Critical Minerals",
    x = "Applications",
    y = "Frequency"
  ) +
  theme_minimal() +
  theme_minimal() +
  theme(
   panel.grid.major = element_line(color = "gray100", size = 0.25),  # Fade out major gridlines
   panel.grid.minor = element_blank()  # Remove minor gridlines
  )

## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Major Source Countries for Critical Minerals

This plot shows the number of critical minerals supplied by each country.

library(dplyr)
library(ggplot2)

# Load the CSV file
commodity_count_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_Fig3_Major_Import_Sources.csv")

colnames(commodity_count_data) <- c("Country", "Commodity_Count", "Map_Class")

# Inspect the data to ensure it is loaded correctly
head(commodity_count_data)

##     Country Commodity_Count Map_Class
## 1 Australia               6    4 to 6
## 2   Austria               2    1 to 3
## 3   Belarus               1    1 to 3
## 4   Belgium               8   7 to 12
## 5   Bolivia               3    1 to 3
## 6    Brazil              10   7 to 12

# Define updated classifications for countries (including Switzerland and Philippines as Allies)
country_classification <- list(
  "Australia" = "Ally", "Canada" = "Ally", "United States" = "Ally", "Germany" = "Ally",
  "France" = "Ally", "Japan" = "Ally", "Belgium" = "Ally", "United Kingdom" = "Ally",
  "Austria" = "Ally", "South Korea" = "Ally", "Israel" = "Ally", "Norway" = "Ally",
  "Switzerland" = "Ally", "Philippines" = "Ally", # Added Switzerland and Philippines as Allies
  "China" = "Competitor", "Russia" = "Competitor", "Belarus" = "Competitor",
  "India" = "Neutral", "Mexico" = "Neutral", "Brazil" = "Neutral", "South Africa" = "Neutral",
  "Kazakhstan" = "Neutral", "Morocco" = "Neutral", "Malaysia" = "Neutral", "Vietnam" = "Neutral",
  "Peru" = "Neutral", "Jamaica" = "Neutral", "Indonesia" = "Neutral", "Bolivia" = "Neutral",
  "Ukraine" = "Ally", "Taiwan" = "Ally", "Poland" = "Ally", "Netherlands" = "Ally",
  "Finland" = "Ally", "Italy" = "Ally", "Ireland" = "Ally", "Unknown" = "Unknown"
)

# Add classification column to the data
commodity_count_data <- commodity_count_data %>%
  mutate(
    Classification = country_classification[Country] %||% "Unknown",
    Classification = factor(Classification, levels = c("Ally", "Competitor", "Neutral", "Unknown")) # Convert to factor
  )

# Plot the data
ggplot(commodity_count_data, aes(x = reorder(Country, -Commodity_Count), y = Commodity_Count, fill = Classification)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_manual(
    values = c("Ally" = "#117733", "Competitor" = "#CC6677", "Neutral" = "#88CCEE", "Unknown" = "gray"),
    labels = c("Ally", "Competitor", "Neutral", "Unknown")
  ) +
  labs(
    title = "Major Source Countries for Critical Minerals (Classification)",
    x = "Country",
    y = "Number of Critical Minerals Supplied",
    fill = "Country Classification"
  ) +
  theme_minimal() +
  theme(
   panel.grid.major = element_line(color = "gray99", size = 0.25),  # Fade out major gridlines
   panel.grid.minor = element_blank()  # Remove minor gridlines
  )

Range of Strategic Minerals by Allies, Competitors, and Neutral Countries

library(dplyr)
library(ggplot2)

# Load the CSV file
commodity_count_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_Fig3_Major_Import_Sources.csv")

colnames(commodity_count_data) <- c("Country", "Commodity_Count", "Map_Class")

# Define updated classifications for countries
country_classification <- list(
  "Australia" = "Ally", "Canada" = "Ally", "United States" = "Ally", "Germany" = "Ally",
  "France" = "Ally", "Japan" = "Ally", "Belgium" = "Ally", "United Kingdom" = "Ally",
  "Austria" = "Ally", "South Korea" = "Ally", "Israel" = "Ally", "Norway" = "Ally",
  "Switzerland" = "Ally", "Philippines" = "Ally",
  "China" = "Competitor", "Russia" = "Competitor", "Belarus" = "Competitor",
  "India" = "Neutral", "Mexico" = "Neutral", "Brazil" = "Neutral", "South Africa" = "Neutral",
  "Kazakhstan" = "Neutral", "Morocco" = "Neutral", "Malaysia" = "Neutral", "Vietnam" = "Neutral",
  "Peru" = "Neutral", "Jamaica" = "Neutral", "Indonesia" = "Neutral", "Bolivia" = "Neutral",
  "Ukraine" = "Ally", "Taiwan" = "Ally", "Poland" = "Ally", "Netherlands" = "Ally",
  "Finland" = "Ally", "Italy" = "Ally", "Ireland" = "Ally", "Unknown" = "Unknown"
)

# Add classification column to the data
commodity_count_data <- commodity_count_data %>%
  mutate(
    Classification = country_classification[Country] %||% "Unknown",
    Classification = factor(Classification, levels = c("Ally", "Competitor", "Neutral", "Unknown")),
    Map_Class = factor(Map_Class, levels = c("1 to 3", "4 to 6", "7 to 12", "19 to 24")) # Order the Map_Class levels
  )

# Aggregate data by Classification and Map_Class
reliance_summary <- commodity_count_data %>%
  group_by(Classification, Map_Class) %>%
  summarize(Total_Commodities = sum(Commodity_Count), .groups = "drop")

# Stacked bar chart
ggplot(reliance_summary, aes(x = Classification, y = Total_Commodities, fill = Map_Class)) +
  geom_bar(stat = "identity", position = "stack", color = "black", size = 0.6, 
           aes(alpha = ifelse(Classification %in% c("Competitor", "Neutral"), 1, 0.3))) +
  scale_fill_manual(
    values = c("1 to 3" = "#F4A582", "4 to 6" = "#92C5DE", "7 to 12" = "#D5A6BD", "19 to 24" = "#66C2A5"),
    breaks = c("1 to 3", "4 to 6", "7 to 12", "19 to 24") # Ensure legend order matches
  ) +
  scale_alpha_identity() +
  labs(
    title = "Reliance on Allies, Competitors, and Neutral Countries",
    x = "Country Classification",
    y = "Total Number of Critical Minerals Supplied",
    fill = "Range of Minerals Supplied"
  ) +
  theme_minimal() +
  theme(
    legend.position = "right", # Position legend for better visibility
    panel.grid = element_blank() # Remove grid lines
  )

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

Geographic illustration of suppliers by category

library(ggplot2)
library(purrr)
library(maps)

## 
## Attaching package: 'maps'

## The following object is masked from 'package:purrr':
## 
##     map

library(dplyr)

# World map data
world <- map_data("world")

# Add U.S. as an Importer to geo_data
geo_data <- commodity_count_data %>%
  bind_rows(data.frame(
    Country = "USA",  # Ensure naming aligns with the `world` data
    Commodity_Count = NA,  # U.S. is an importer, not a supplier
    Map_Class = NA,
    Classification = "Importer"
  )) %>%
  mutate(Classification = case_when(
    is.na(Classification) & !is.na(Commodity_Count) ~ "Other Supplier",
    TRUE ~ Classification
  ))

# Merge with world map data
world_map <- world %>%
  left_join(geo_data, by = c("region" = "Country")) %>%
  mutate(Classification = replace_na(Classification, "Not a Supplier"))

# Plot the map
ggplot(world_map, aes(x = long, y = lat, group = group, fill = Classification)) +
  geom_polygon(color = "white") +
  scale_fill_manual(
    values = c(
      "Importer" = "#F0C808",
      "Ally" = "#117733",
      "Competitor" = "#CC6677",
      "Neutral" = "#88CCEE",
      "Other Supplier" = "#9467BD",
      "Not a Supplier" = "lightgrey"
    )
  ) +
  labs(
    title = "Global Suppliers of Critical Minerals with U.S. as Importer",
    fill = "Country Classification"
  ) +
  theme_minimal() +
  theme(
    axis.text = element_blank(),        # Remove axis text
    axis.ticks = element_blank(),       # Remove axis ticks
    axis.title = element_blank(),       # Remove axis labels
    panel.grid = element_blank(),       # Remove grid lines
    legend.position = "bottom"          # Move legend to the bottom
  )

Visualization of Top Minerals with the Highest Shortfall by Leading Supplier

# 4. Strategic Gaps in Domestic Production
# Clean numeric data and calculate shortfall
domestic_production_gap <- domestic_production_gap %>%
  mutate(
    Primary_prod = as.numeric(gsub("[^0-9\\.]", "", Primary_prod)),
    Apparent_Consuption = as.numeric(gsub("[^0-9\\.]", "", Apparent_Consuption)),
    Shortfall = Apparent_Consuption - Primary_prod
  ) %>%
  filter(!is.na(Shortfall))

# Plot with highlighted and faded bars
domestic_production_gap %>%
  arrange(desc(Shortfall)) %>%
  head(10) %>%
  ggplot(aes(x = reorder(Critical_mineral, -Shortfall), y = Shortfall)) +
  geom_bar(
    stat = "identity", 
    aes(fill = ifelse(Critical_mineral %in% c("Zinc", "Manganese", "Chromium"), "Highlight", "Fade"))
  ) +
  scale_fill_manual(
    values = c("Highlight" = "#CC6677", "Fade" = "lightgrey"),  # Highlighted and faded colors
    guide = "none"  # Remove legend for fill
  ) +
  coord_flip() +
  labs(
    title = "Strategic Gaps in Domestic Production for Top Minerals",
    x = "Critical Mineral",
    y = "Shortfall (Metric Tons)"
  ) +
  theme_minimal() +
  theme(
    panel.grid = element_blank(),  # Remove grid lines
    axis.text.y = element_text(size = 10)  # Adjust axis label size
  )

glimpse(domestic_production_gap)

## Rows: 20
## Columns: 14
## $ Source                       <chr> "MCS2024", "MCS2024", "MCS2024", "MCS2024…
## $ Year                         <chr> "2023_estimated", "2023_estimated", "2023…
## $ Critical_mineral             <chr> "Antimony", "Arsenic", "Beryllium", "Bism…
## $ Primary_prod                 <dbl> 0, 0, 190, 0, 0, 500, 0, 0, 0, 0, 17000, …
## $ Secondary_prod               <chr> "4000", NA, NA, "80", "100000", "2100", "…
## $ Apparent_Consuption          <dbl> 22000, 6400, 150, 1400, 380000, 6400, 19,…
## $ Net_Import_Reliance          <chr> "82", "100", NA, "94", "74", "67", "100",…
## $ Primary_import_source        <chr> "China", "China", "Kazakhstan", "China", …
## $ Leading_source_country       <chr> "China", "Peru", "United States", "China"…
## $ Leading_country_prod         <int> 40000, 27000, 190, 16000, 18000000, 17000…
## $ World_total_prod             <int> 83000, 60000, 330, 20000, 41000000, 23000…
## $ World_prod_notes             <chr> "", "Arsenic trioxide.", "", "", "", "", …
## $ Leading_source_precent_world <int> 48, 45, 58, 80, 44, 74, 98, 77, 66, 36, 5…
## $ Shortfall                    <dbl> 22000, 6400, -40, 1400, 380000, 5900, 19,…

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(scales)  # For formatting the axis labels

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

# Load the data (update the file path as needed)
domestic_production_gap <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_T5_Critical_Minerals_Salient.csv")

# Clean the numeric data and calculate shortfall
domestic_production_gap <- domestic_production_gap %>%
  mutate(
    Primary_prod = as.numeric(gsub("[^0-9\\.]", "", Primary_prod)),
    Apparent_Consuption = as.numeric(gsub("[^0-9\\.]", "", Apparent_Consuption)),
    Shortfall = Apparent_Consuption - Primary_prod
  ) %>%
  filter(!is.na(Shortfall))

# Plot the top shortfalls
domestic_production_gap %>%
  arrange(desc(Shortfall)) %>%
  head(10) %>%
  ggplot(aes(x = reorder(Critical_mineral, -Shortfall), y = Shortfall)) +
  geom_bar(stat = "identity", fill = "#CC6677") +
  coord_flip() +
  labs(
    title = "Strategic Gaps in Domestic Production for Top Minerals",
    x = "Critical Mineral",
    y = "Shortfall (Metric Tons)"
  ) +
  scale_y_continuous(labels = label_comma()) +  # Format the y-axis with commas
  theme_minimal()

# Load necessary libraries
library(dplyr)
library(tidyr)

# Debug: Inspect the column names
print("Column names in reliance_data:")

## [1] "Column names in reliance_data:"

print(colnames(reliance_data))

## [1] "Source"                         "Import_Share_Order"            
## [3] "Commodity"                      "Net_Import_Reliance_pct_2023"  
## [5] "Major_Import_Sources_2019_2022" "Notes"

# Ensure correct column name
# Replace with the actual column name if different
correct_column_name <- "Major_Import_Sources_2019_2022" # Adjust this if necessary

# Check if the column exists in the dataset
if (!(correct_column_name %in% colnames(reliance_data))) {
  stop(paste("Column", correct_column_name, "does not exist in reliance_data."))
}

# Break up countries and assign reliance rank
reliance_data_expanded <- reliance_data %>%
  mutate(
    # Ensure the column is properly split into a list of countries
    Countries = strsplit(as.character(.data[[correct_column_name]]), ",\\s*")
  ) %>%
  unnest(Countries) %>%  # Expand the list into individual rows
  group_by(Commodity) %>%  # Group by the mineral/commodity
  mutate(
    reliance_rank = row_number()  # Rank the countries within each commodity group
  ) %>%
  ungroup()

# View the resulting dataset
glimpse(reliance_data_expanded)

## Rows: 228
## Columns: 8
## $ Source                         <chr> "MCS2024", "MCS2024", "MCS2024", "MCS20…
## $ Import_Share_Order             <int> 1, 1, 1, 1, 2, 2, 3, 4, 4, 4, 4, 5, 5, …
## $ Commodity                      <chr> "ARSENIC, all forms", "ARSENIC, all for…
## $ Net_Import_Reliance_pct_2023   <int> 100, 100, 100, 100, 100, 100, 100, 100,…
## $ Major_Import_Sources_2019_2022 <chr> "China, Morocco, Malaysia, Belgium", "C…
## $ Notes                          <chr> "Descending order of import share. Impo…
## $ Countries                      <chr> "China", "Morocco", "Malaysia", "Belgiu…
## $ reliance_rank                  <int> 1, 2, 3, 4, 1, 2, 1, 1, 2, 3, 4, 1, 2, …

Import Reliance Rack and Frequency of Countries in each Rank

# Load necessary libraries
library(dplyr)
library(ggplot2)

# Calculate the proportion of reliance ranks for each country
country_reliance_rank <- reliance_data_expanded %>%
  group_by(Countries, reliance_rank) %>%  # Group by country and reliance rank
  summarize(count = n(), .groups = "drop") %>%  # Count occurrences of each rank per country
  group_by(Countries) %>%
  mutate(proportion = count / sum(count)) %>%  # Calculate proportion of each rank per country
  ungroup()

# Ensure reliance_rank is treated as a factor
country_reliance_rank <- country_reliance_rank %>%
  mutate(reliance_rank = as.factor(reliance_rank))  # Convert reliance_rank to a factor

# Calculate the number of minerals each country is involved with
country_counts <- country_reliance_rank %>%
  group_by(Countries) %>%
  summarize(total_minerals = sum(count), .groups = "drop")  # Total count of minerals per country

# Reorder countries based on the total reliance count
country_order <- country_counts %>%
  arrange(desc(total_minerals)) %>%
  pull(Countries)  # Extract countries ordered by total minerals

# Update `country_reliance_rank` with reordered country levels
country_reliance_rank <- country_reliance_rank %>%
  mutate(Countries = factor(Countries, levels = country_order))

# Prepare data: Calculate the frequency of each country in each reliance rank
ranked_country_frequencies <- country_reliance_rank %>%
  group_by(reliance_rank, Countries) %>%
  summarize(frequency = sum(count), .groups = "drop")  # Count occurrences of each rank for each country

# Accessible color palette
accessible_colors <- c(
  "1" = "#117733",        # Dark green
  "4" = "#CC6677",        # Reddish
  "2" = "#88CCEE",        # Light blue
  "3" = "#999999"         # Gray
)

# Create bar charts, one for each rank
ggplot(ranked_country_frequencies, aes(x = reorder(Countries, -frequency), y = frequency, fill = reliance_rank)) +
  geom_bar(stat = "identity") +
  facet_wrap(~ reliance_rank, scales = "free_y") +  # Create a facet for each rank
  labs(
    title = "Frequency of Countries in Each Reliance Rank (Top Four Ranks)",
    x = "Country",
    y = "Frequency",
    fill = "Reliance Rank"
  ) +
  scale_fill_manual(values = accessible_colors) +  # Use the accessible color palette
  theme_minimal() +
  theme(
    axis.text.x = element_text(size = 5, angle = 90, hjust = 1, vjust = 1),  # Adjust alignment for better readability
    axis.text.y = element_text(size = 10),
    strip.text = element_text(size = 10, face = "bold"),
    legend.position = "none",  # Hide legend as reliance rank is already shown in facets
    panel.grid.major = element_line(color = "gray99", size = 0.25),  # Fade out major gridlines
    panel.grid.minor = element_blank()  # Remove minor gridlines
    # panel.grid = element_blank()  # Remove all gridlines

  )

# Load necessary libraries
library(treemapify)
library(ggplot2)

# Prepare data for treemap
treemap_data <- country_reliance_rank %>%
  group_by(Countries) %>%
  mutate(
    total_minerals = sum(count),
    reliance_rank = as.factor(reliance_rank)
  ) %>%
  ungroup()

# Create the treemap
ggplot(treemap_data, aes(
  area = total_minerals,
  fill = reliance_rank,
  label = paste(Countries, "(", total_minerals, ")", sep = "")
)) +
  geom_treemap() +
  geom_treemap_text(
    colour = "white",
    place = "centre",
    grow = TRUE
  ) +
  scale_fill_brewer(palette = "Set3", name = "Reliance Rank") +
  labs(
    title = "Treemap of Reliance Ranks by Country",
    subtitle = "Size represents the number of minerals; color shows reliance rank proportion"
  ) +
  theme_minimal()

# Create a bubble chart
reliance_data %>%
  ggplot(aes(
    x = Net_Import_Reliance_pct_2023,
    y = reorder(Commodity, Net_Import_Reliance_pct_2023),
    size = Net_Import_Reliance_pct_2023,
    color = Net_Import_Reliance_pct_2023
  )) +
  geom_point(alpha = 0.7, size = 1.5) +
  scale_color_gradient(low = "green", high = "red") +
  labs(
    title = "U.S. Net Import Reliance on Critical Minerals (2023)",
    x = "Net Import Reliance (%)",
    y = "Critical Mineral",
    size = "Reliance (%)",
    color = "Reliance (%)"
  ) +
  theme_minimal() +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 5),  # Reduce y-axis font size
    panel.grid.major = element_blank(),   # Remove major gridlines
    panel.grid.minor = element_blank(),   # Remove minor gridlines
    panel.background = element_blank()    # Ensure no background grid
  )

# Create a customized lollipop chart
reliance_data %>%
  ggplot(aes(x = Net_Import_Reliance_pct_2023, y = reorder(Commodity, Net_Import_Reliance_pct_2023))) +
  geom_segment(aes(xend = 0, yend = Commodity), color = "gray") +
  geom_point(aes(color = Net_Import_Reliance_pct_2023), size = 1) +
  scale_color_gradient(low = "green", high = "red") +
  labs(
    title = "Lollipop Chart of U.S. Net Import Reliance on Critical Minerals (2023)",
    x = "Net Import Reliance (%)",
    y = "Critical Mineral",
    color = "Reliance (%)"
  ) +
  theme_minimal() +
  theme(
    axis.text.y = element_text(size = 5),  # Reduce y-axis font size
    panel.grid.major = element_blank(),   # Remove major gridlines
    panel.grid.minor = element_blank(),   # Remove minor gridlines
    panel.background = element_blank()    # Ensure no background grid
  )

Major Source Countries for Critical Minerals

This plot shows the number of critical minerals supplied by each country.

# Load the data (update the file path as needed)
source_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_Fig3_Major_Import_Sources.csv")

# Create a bar chart for countries supplying critical minerals
source_data %>%
  arrange(desc(Commodity_Count)) %>%
  ggplot(aes(x = reorder(Country, -Commodity_Count), y = Commodity_Count)) +
  geom_bar(stat = "identity", fill = "#CC6677") +
  coord_flip() +
  labs(
    title = "Major Source Countries for Critical Minerals",
    x = "Country",
    y = "Number of Critical Minerals Supplied"
  ) +
  theme_minimal()

library(dplyr)

library(dplyr)

# Load Data
end_use_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_T4_Critical_Minerals_End_Use.csv")
reliance_data <- read.csv("https://raw.githubusercontent.com/hawa1983/DATA-608/refs/heads/main/Story%207/MCS2024_Fig2_Net_Import_Reliance.csv")

# Print Data Overview
cat("\nMajor Import Source\n")

## 
## Major Import Source

glimpse(commodity_count_data)

## Rows: 43
## Columns: 4
## $ Country         <chr> "Australia", "Austria", "Belarus", "Belgium", "Bolivia…
## $ Commodity_Count <int> 6, 2, 1, 8, 3, 10, 23, 2, 24, 1, 1, 2, 1, 1, 1, 12, 1,…
## $ Map_Class       <fct> 4 to 6, 1 to 3, 1 to 3, 7 to 12, 1 to 3, 7 to 12, 19 t…
## $ Classification  <fct> Ally, Ally, Competitor, Ally, Neutral, Neutral, Ally, …

cat("\nDomestic Production Gap\n")

## 
## Domestic Production Gap

glimpse(domestic_production_gap)

## Rows: 20
## Columns: 14
## $ Source                       <chr> "MCS2024", "MCS2024", "MCS2024", "MCS2024…
## $ Year                         <chr> "2023_estimated", "2023_estimated", "2023…
## $ Critical_mineral             <chr> "Antimony", "Arsenic", "Beryllium", "Bism…
## $ Primary_prod                 <dbl> 0, 0, 190, 0, 0, 500, 0, 0, 0, 0, 17000, …
## $ Secondary_prod               <chr> "4000", NA, NA, "80", "100000", "2100", "…
## $ Apparent_Consuption          <dbl> 22000, 6400, 150, 1400, 380000, 6400, 19,…
## $ Net_Import_Reliance          <chr> "82", "100", NA, "94", "74", "67", "100",…
## $ Primary_import_source        <chr> "China", "China", "Kazakhstan", "China", …
## $ Leading_source_country       <chr> "China", "Peru", "United States", "China"…
## $ Leading_country_prod         <int> 40000, 27000, 190, 16000, 18000000, 17000…
## $ World_total_prod             <int> 83000, 60000, 330, 20000, 41000000, 23000…
## $ World_prod_notes             <chr> "", "Arsenic trioxide.", "", "", "", "", …
## $ Leading_source_precent_world <int> 48, 45, 58, 80, 44, 74, 98, 77, 66, 36, 5…
## $ Shortfall                    <dbl> 22000, 6400, -40, 1400, 380000, 5900, 19,…

cat("\nCritical Minerals End Use\n")

## 
## Critical Minerals End Use

glimpse(end_use_data)

## Rows: 50
## Columns: 4
## $ Source               <chr> "MCS2024", "MCS2024", "MCS2024", "MCS2024", "MCS2…
## $ Critical.Mineral     <chr> "Aluminum", "Antimony", "Arsenic", "Barite", "Ber…
## $ Primary.Applications <chr> "Metallurgy and many sectors of the economy.", "F…
## $ Category_Note        <chr> "", "", "", "", "", "", "Included in the Rare Ear…

cat("\nNet Import Reliance\n")

## 
## Net Import Reliance

glimpse(reliance_data)

## Rows: 63
## Columns: 6
## $ Source                         <chr> "MCS2024", "MCS2024", "MCS2024", "MCS20…
## $ Import_Share_Order             <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, …
## $ Commodity                      <chr> "ARSENIC, all forms", "ASBESTOS", "CESI…
## $ Net_Import_Reliance_pct_2023   <int> 100, 100, 100, 100, 100, 100, 100, 100,…
## $ Major_Import_Sources_2019_2022 <chr> "China, Morocco, Malaysia, Belgium", "B…
## $ Notes                          <chr> "Descending order of import share. Impo…

Visualization of Top Minerals with the Highest Shortfall by Leading Supplier

library(ggplot2)
library(dplyr)
library(janitor)

## 
## Attaching package: 'janitor'

## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test

# Clean column names to ensure consistency
domestic_production_gap <- domestic_production_gap %>%
  clean_names() # Standardizes all column names to lowercase with underscores

# Replace NA in leading_source_country with "Unknown"
domestic_production_gap <- domestic_production_gap %>%
  mutate(leading_source_country = ifelse(is.na(leading_source_country), "Unknown", leading_source_country))

# Verify the correct column name
top_shortfall <- domestic_production_gap %>%
  arrange(desc(shortfall)) %>%   # Use cleaned column names (all lowercase)
  slice(1:10)

# Plotting
ggplot(top_shortfall, aes(x = reorder(critical_mineral, -shortfall), y = shortfall, fill = leading_source_country)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top Minerals with the Highest Shortfall",
    subtitle = "Highlighting Leading Suppliers",
    x = "Critical Mineral",
    y = "Shortfall (Metric Tons)",
    fill = "Leading Supplier"
  ) +
  theme_minimal()

Visualization of Net Import Reliance Distribution

This shows the distribution of Net_Import_Reliance_pct_2023 for critical minerals.

ggplot(reliance_data, aes(x = Net_Import_Reliance_pct_2023)) +
  geom_histogram(binwidth = 10, fill = "skyblue", color = "black") +
  labs(
    title = "Distribution of Net Import Reliance for Critical Minerals",
    x = "Net Import Reliance (%)",
    y = "Frequency"
  ) +
  theme_minimal()

Visualization of Top Supplying Countries by Mineral Count and Classification

This highlights countries supplying the most minerals categorized as Ally, Competitor, or Neutral.

top_countries <- commodity_count_data %>%
  arrange(desc(Commodity_Count)) %>%
  slice(1:10)

ggplot(top_countries, aes(x = reorder(Country, Commodity_Count), y = Commodity_Count, fill = Classification)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  scale_fill_manual(values = c("Ally" = "green", "Competitor" = "red", "Neutral" = "blue")) +
  labs(
    title = "Top Supplying Countries by Mineral Count",
    x = "Country",
    y = "Number of Critical Minerals Supplied",
    fill = "Country Classification"
  ) +
  theme_minimal()

Conclusion Slide

# Load Required Libraries
library(ggplot2)
library(dplyr)
library(janitor)
library(rnaturalearth)
library(rnaturalearthdata)

## 
## Attaching package: 'rnaturalearthdata'

## The following object is masked from 'package:rnaturalearth':
## 
##     countries110

library(sf)

## Linking to GEOS 3.12.2, GDAL 3.9.3, PROJ 9.4.1; sf_use_s2() is TRUE

# Step 1: Data Cleaning ---------------------------------------------------
# Clean column names and net_import_reliance
domestic_production_gap <- domestic_production_gap %>% 
  clean_names() %>%
  mutate(
    net_import_reliance = as.numeric(gsub("[<>]", "", net_import_reliance)),
    net_import_reliance = ifelse(is.na(net_import_reliance), 0, net_import_reliance),
    shortfall = ifelse(is.na(shortfall), 0, shortfall),
    leading_source_country = ifelse(is.na(leading_source_country), "Unknown", leading_source_country)
  )

# Step 2: Aggregate Data --------------------------------------------------
country_summary <- domestic_production_gap %>%
  group_by(leading_source_country) %>%
  summarize(
    total_shortfall = sum(shortfall, na.rm = TRUE),
    average_reliance = mean(net_import_reliance, na.rm = TRUE)
  )

# Step 3: Prepare World Map -----------------------------------------------
# Load world map
world <- map_data("world")

# Add the U.S. as Importer and classify other suppliers
geo_data <- country_summary %>%
  bind_rows(data.frame(
    leading_source_country = "USA",
    total_shortfall = NA,
    average_reliance = NA,
    Classification = "Importer"
  )) %>%
  mutate(
    Classification = case_when(
      leading_source_country %in% c("China", "Russia") ~ "Competitor",
      leading_source_country %in% c("Canada", "Australia") ~ "Ally",
      leading_source_country %in% c("Brazil", "South Africa") ~ "Other Supplier",
      is.na(Classification) ~ "Other Supplier",
      TRUE ~ Classification
    )
  )

# Merge with world map data
world_map <- world %>%
  left_join(geo_data, by = c("region" = "leading_source_country")) %>%
  mutate(Classification = replace_na(Classification, "Not a Supplier"))

# Add longitude/latitude centroids for bubbles
bubble_data <- world_map %>%
  group_by(region) %>%
  summarize(
    long = mean(long, na.rm = TRUE),
    lat = mean(lat, na.rm = TRUE),
    average_reliance = mean(average_reliance, na.rm = TRUE)
  ) %>%
  filter(!is.na(average_reliance))

# Step 4: Plot the Map with Bubbles ---------------------------------------
ggplot(world_map, aes(x = long, y = lat, group = group, fill = Classification)) +
  # Country polygons
  geom_polygon(color = "white") +
  # Add bubbles (remove size legend explicitly)
  geom_point(data = bubble_data, aes(x = long, y = lat, color = average_reliance, size = average_reliance),
             inherit.aes = FALSE, show.legend = c(color = TRUE, size = FALSE)) +
  # Scales
  scale_fill_manual(
    values = c(
      "Importer" = "#F0C808",
      "Ally" = "#117733",
      "Competitor" = "#CC6677",
      "Neutral" = "#88CCEE",
      "Other Supplier" = "#9467BD",
      "Not a Supplier" = "lightgrey"
    ),
    name = "Country Classification"
  ) +
  scale_color_viridis_c(name = "Average Net Import\nReliance (%)") +
  # Labels
  labs(
    title = "Global Suppliers of Critical Minerals with U.S. as Importer",
    subtitle = "Classified by Role and Net Import Reliance",
    caption = "Data Source: USGS Mineral Commodity Summaries 2024"
  ) +
  # Theme adjustments
  theme_minimal() +
  theme(
    axis.text = element_blank(),        
    axis.ticks = element_blank(),       
    axis.title = element_blank(),       
    panel.grid = element_blank(),       
    legend.position = "right"
  )

Transition Slides

# Load Libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(maps)
library(gridExtra)

## 
## Attaching package: 'gridExtra'

## The following object is masked from 'package:dplyr':
## 
##     combine

library(grid)
library(ggrepel)

# Simulated Data ----------------------------------------------------------
# Bar Chart Data (Applications of Critical Minerals)
bar_data <- data.frame(
  Category = c("Aerospace &\n Defense", "Batteries & Energy", 
               "Catalysts & Industrial", "Electronics & Optics",
               "Magnets & Lasers", "Steelmaking &\n Alloys"),
  Count = c(8, 12, 9, 11, 7, 10),
  Color = c("#1f78b4", "#ff7f00", "#e31a1c", "#6a3d9a", "#33a02c", "#b2df8a")
)

# World Map Data for Supplier Classification
world <- map_data("world")
supplier_regions <- data.frame(
  region = c("USA", "China", "Russia", "Australia", "Brazil", "India"),
  Classification = c("Ally", "Competitor", "Competitor", "Ally", "Other Supplier", "Neutral")
)

# Merge Supplier Data with Map
world_map <- world %>%
  left_join(supplier_regions, by = "region") %>%
  mutate(Classification = replace_na(Classification, "Not a Supplier"))

# Chemical Symbols for Scattering
set.seed(123) # Reproducibility
chemical_symbols <- data.frame(
  symbol = sample(c("Al", "Sb", "As", "B", "Be", "Bi", "Ce", "Cs", "Co", 
                    "Cr", "Cu", "Dy", "Er", "Eu", "F", "Gd", "Ga", "Ge",
                    "Hf", "Ho", "In", "Ir", "La", "Li", "Mg", "Mn", "Nd", 
                    "Ni", "Nb", "Pd", "Pt", "Pr", "Rh", "Ru", "Sm", "Sc",
                    "Ta", "Te", "Tb", "Tm", "Sn", "Ti", "W", "V", "Yb", 
                    "Y", "Zn", "Zr"), 50, replace = TRUE),
  x = runif(50, 0, 1),
  y = runif(50, 0, 1),
  color = sample(colors(), 50)
)

# Plot 1: Bar Chart -------------------------------------------------------
bar_chart <- ggplot(bar_data, aes(x = Category, y = Count, fill = Category)) +
  geom_bar(stat = "identity", width = 0.7) +
  geom_text(aes(label = Category, size = Count, color = Color), 
            position = position_stack(vjust = 1.05), 
            hjust = 0.5, fontface = "bold") +
  scale_fill_manual(values = bar_data$Color) +
  scale_color_identity() +
  scale_size_continuous(range = c(3, 10)) +
  theme_void() +
  theme(legend.position = "none")

# Plot 2: World Map -------------------------------------------------------
map_plot <- ggplot(world_map, aes(x = long, y = lat, group = group, fill = Classification)) +
  geom_polygon(color = "white") +
  scale_fill_manual(values = c(
    "Ally" = "#117733",
    "Competitor" = "#CC6677",
    "Neutral" = "#88CCEE",
    "Other Supplier" = "#9467BD",
    "Not a Supplier" = "lightgrey"
  )) +
  theme_void() +
  theme(legend.position = "none")

# Overlay Chemical Symbols ------------------------------------------------
scatter_plot <- ggplot() +
  geom_text(data = chemical_symbols, 
            aes(x = x, y = y, label = symbol, color = color), 
            size = 6, fontface = "bold") +
  scale_color_identity() +
  theme_void()

# Combine Plots -----------------------------------------------------------
grid.arrange(
  bar_chart,
  map_plot,
  scatter_plot,
  ncol = 1,
  heights = c(3, 3, 1)
)

# Load Libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(maps)
library(gridExtra)
library(grid)
library(ggrepel)

# Simulated Data ----------------------------------------------------------
# Bar Chart Data (Applications of Critical Minerals)
bar_data <- data.frame(
  Category = c("Aerospace &\nDefense", "Batteries & Energy", 
               "Catalysts & Industrial", "Electronics & Optics",
               "Magnets & Lasers", "Steelmaking &\nAlloys"),
  Count = c(8, 12, 9, 11, 7, 10),
  Color = c("#1f78b4", "#ff7f00", "#e31a1c", "#6a3d9a", "#33a02c", "#b2df8a")
)

# World Map Data for Supplier Classification
world <- map_data("world")
supplier_regions <- data.frame(
  region = c("USA", "China", "Russia", "Australia", "Brazil", "India"),
  Classification = c("Ally", "Competitor", "Competitor", "Ally", "Other Supplier", "Neutral")
)

# Merge Supplier Data with Map
world_map <- world %>%
  left_join(supplier_regions, by = "region") %>%
  mutate(Classification = replace_na(Classification, "Not a Supplier"))

# Chemical Symbols for Scattering (Now scaled to the map area)
set.seed(123) # Reproducibility
chemical_symbols <- data.frame(
  symbol = sample(c("Al", "Sb", "As", "B", "Be", "Bi", "Ce", "Cs", "Co", 
                    "Cr", "Cu", "Dy", "Er", "Eu", "F", "Gd", "Ga", "Ge",
                    "Hf", "Ho", "In", "Ir", "La", "Li", "Mg", "Mn", "Nd", 
                    "Ni", "Nb", "Pd", "Pt", "Pr", "Rh", "Ru", "Sm", "Sc",
                    "Ta", "Te", "Tb", "Tm", "Sn", "Ti", "W", "V", "Yb", 
                    "Y", "Zn", "Zr"), 50, replace = TRUE),
  long = runif(50, -180, 180),
  lat = runif(50, -60, 80),
  color = sample(colors(), 50)
)

# Plot 1: Bar Chart -------------------------------------------------------
bar_chart <- ggplot(bar_data, aes(x = Category, y = Count, fill = Category)) +
  geom_bar(stat = "identity", width = 0.7) +
  geom_text(aes(label = gsub("&", "and", Category), color = Color, size = Count), 
            position = position_stack(vjust = 1.4), 
            fontface = "bold", hjust = 0.5) +
  scale_fill_manual(values = bar_data$Color) +
  scale_color_identity() +
  scale_size_continuous(range = c(3, 8)) +
  theme_void() +
  theme(legend.position = "none")

# Plot 2: World Map -------------------------------------------------------
map_plot <- ggplot(world_map, aes(x = long, y = lat, group = group, fill = Classification)) +
  geom_polygon(color = "white") +
  scale_fill_manual(values = c(
    "Ally" = "#117733",
    "Competitor" = "#CC6677",
    "Neutral" = "#88CCEE",
    "Other Supplier" = "#9467BD",
    "Not a Supplier" = "lightgrey"
  )) +
  theme_void() +
  theme(legend.position = "none")

# Plot 3: Chemical Symbols Scattered --------------------------------------
scatter_plot <- ggplot() +
  geom_text(data = chemical_symbols, 
            aes(x = long, y = lat, label = symbol, color = color), 
            size = 5, fontface = "bold") +
  scale_color_identity() +
  theme_void()

# Combine Plots -----------------------------------------------------------
grid.arrange(
  bar_chart,
  map_plot,
  scatter_plot,
  ncol = 1,
  heights = c(3, 3, 3)
)

# Load Libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(grid)
library(maps)
library(ggrepel)

# Simulated Data ----------------------------------------------------------
# Data for Sources - Bar Chart
sources_data <- data.frame(
  Source = c("Recycling", "Domestic Mining", "Imports"),
  Count = c(15, 10, 25),
  Color = c("#1f78b4", "#33a02c", "#e31a1c")
)

# World Map Data for Supplier Roles
world <- map_data("world")
supplier_roles <- data.frame(
  region = c("USA", "China", "Russia", "Australia", "Brazil", "India"),
  Classification = c("Ally", "Competitor", "Competitor", "Ally", "Other Supplier", "Neutral")
)

# Merge Supplier Data with Map
world_map <- world %>%
  left_join(supplier_roles, by = "region") %>%
  mutate(Classification = replace_na(Classification, "Not a Supplier"))

# Scatter Chemical Symbols
set.seed(123)
chemical_symbols <- data.frame(
  symbol = sample(c("Al", "Co", "Ni", "Li", "Mn", "Cr", "Zn", "Be", "Nd", "W", "Y", "Pt", "Nb"), 30, replace = TRUE),
  x = runif(30, 0, 1),
  y = runif(30, 0, 1),
  color = sample(colors(), 30)
)

# Plot 1: Bar Chart - Sources --------------------------------------------
sources_plot <- ggplot(sources_data, aes(x = Source, y = Count, fill = Source)) +
  geom_bar(stat = "identity", width = 0.6) +
  geom_text(aes(label = Source, size = Count), position = position_stack(vjust = 0.9), 
            color = "white", fontface = "bold") +
  scale_fill_manual(values = sources_data$Color) +
  scale_size_continuous(range = c(3, 8)) +
  theme_void() +
  theme(legend.position = "none")

# Plot 2: World Map - Supplier Roles --------------------------------------
map_plot <- ggplot(world_map, aes(x = long, y = lat, group = group, fill = Classification)) +
  geom_polygon(color = "white") +
  scale_fill_manual(values = c(
    "Ally" = "#117733",
    "Competitor" = "#CC6677",
    "Neutral" = "#88CCEE",
    "Other Supplier" = "#9467BD",
    "Not a Supplier" = "lightgrey"
  )) +
  theme_void() +
  theme(legend.position = "bottom")

# Plot 3: Scatter Symbols - Conclusion ------------------------------------
scatter_plot <- ggplot() +
  geom_text(data = chemical_symbols, aes(x = x, y = y, label = symbol, color = color),
            size = 6, fontface = "bold") +
  scale_color_identity() +
  theme_void()

# Combine All Plots -------------------------------------------------------
grid.arrange(
  sources_plot,       # Top Section: Sources
  map_plot,           # Middle Section: Suppliers & Vulnerabilities
  scatter_plot,       # Bottom Section: Conclusion
  ncol = 1,
  heights = c(2, 3, 2)
)

# Load Libraries
library(ggplot2)
library(dplyr)
library(tidyr)
library(maps)
library(gridExtra)
library(grid)
library(ggrepel)

# Simulated Data ----------------------------------------------------------
# Bar Chart Data (Applications of Critical Minerals)
bar_data <- data.frame(
  Category = c("Aerospace & Defense", "Batteries & Energy", 
               "Catalysts & Industrial", "Electronics & Optics",
               "Magnets & Lasers", "Steelmaking & Alloys"),
  Count = c(60, 88, 75, 90, 82, 75),
  Color = c("#6a3d9a", "#ff7f00", "#1f78b4", "#33a02c", "#e31a1c", "#b2df8a"),
  x = c(1, 2, 3, 4, 5, 6)
)

# World Map Data for Supplier Classification
world <- map_data("world")
supplier_regions <- data.frame(
  region = c("USA", "China", "Russia", "Australia", "Brazil", "India"),
  Classification = c("Ally", "Competitor", "Competitor", "Ally", "Other Supplier", "Neutral")
)

# Merge Supplier Data with Map
world_map <- world %>%
  left_join(supplier_regions, by = "region") %>%
  mutate(Classification = replace_na(Classification, "Not a Supplier"))

# Scatter Words -----------------------------------------------------------
set.seed(123) # For reproducibility
words_data <- data.frame(
  word = c("Applications", "Supply", "Demand", "Imbalance"),
  x = runif(20, 0.5, 6.5), # Random x positions
  y = runif(20, 5, 20),    # Random y positions
  color = sample(colors(), 20)
)

# Plot 1: Bar Chart with Numbers ------------------------------------------
bar_chart <- ggplot(bar_data, aes(x = factor(x), y = Count, fill = Category)) +
  geom_bar(stat = "identity", width = 0.7) +
  geom_text(aes(label = paste0(Count, "%"), y = Count / 2), # Place numbers at centroid
            size = 6, fontface = "bold", color = "black") +
  scale_fill_manual(values = bar_data$Color) +
  theme_void() +
  theme(legend.position = "none")

# Plot 2: World Map -------------------------------------------------------
map_plot <- ggplot(world_map, aes(x = long, y = lat, group = group, fill = Classification)) +
  geom_polygon(color = "white") +
  scale_fill_manual(values = c(
    "Ally" = "#117733",
    "Competitor" = "#CC6677",
    "Neutral" = "#88CCEE",
    "Other Supplier" = "#9467BD",
    "Not a Supplier" = "lightgrey"
  )) +
  theme_void() +
  theme(legend.position = "none")

# Overlay Words -----------------------------------------------------------
words_plot <- ggplot() +
  geom_text(data = words_data, 
            aes(x = x, y = y, label = word, color = color), 
            size = 6, fontface = "bold", angle = sample(c(-30, 0, 30), 20, replace = TRUE)) +
  scale_color_identity() +
  theme_void()

# Combine Plots -----------------------------------------------------------
grid.arrange(
  bar_chart,
  map_plot,
  words_plot,
  ncol = 1,
  heights = c(3, 3, 1)
)

DATA 608 Story 7 - Where Do Strategic Minerals Come From?

Fomba Kassoh

2024-12-15

Load Necessary Libraries

Filter for Selected Minerals

Illustrating strategic use of criticl menerals

Applications of Critical Minerals

This plot highlights the primary applications of critical minerals.

Major Source Countries for Critical Minerals

This plot shows the number of critical minerals supplied by each country.

Range of Strategic Minerals by Allies, Competitors, and Neutral Countries

Geographic illustration of suppliers by category

Visualization of Top Minerals with the Highest Shortfall by Leading Supplier

Import Reliance Rack and Frequency of Countries in each Rank

Major Source Countries for Critical Minerals

This plot shows the number of critical minerals supplied by each country.

Visualization of Top Minerals with the Highest Shortfall by Leading Supplier

Visualization of Net Import Reliance Distribution

Visualization of Top Supplying Countries by Mineral Count and Classification

Conclusion Slide

Transition Slides